This invention relates generally to vibration analysis for the proactive maintenance of rotating machines.
A running motor is a source of vibration. As a motor wears, the vibration level tends to increase. This vibration can be monitored to determine the relative health of that motor and schedule preventive maintenance procedures.
The vibration of a motor occurs over a relatively wide frequency band from low frequencies to higher frequencies. Different problems in a motor (and its load) cause different types of vibration. For instance, rotor imbalance produces an increase in the radial vibration spectrum at 1xc3x97 speed of rotation (first order). Bearing problems usually create an increase in vibration energy at higher frequencies. These higher frequencies are typically in the range of ten times the rotation speed and higher.
A major concern in proactive maintenance is the early detection of bearing rolling element failure. Vibration related to worn bearings usually is created by the multiple impact of metal bearing parts. Accurate analysis of the structure of a bearing provides values of frequencies for each type of defect (inner race, outer race, rolling element, etc.). Calculating the various bearing frequencies also requires information that is typically unknown, for example: inner and outer race diameter, ball size and number of balls. To complicate matters, a bearing manufacturer may change the internal design of a bearing without changing the specifications. They guarantee only the external parameters (size, maximum speed and load). Thus, the methods of vibration monitoring based on the knowledge of exact structure of the bearings could be used only for limited area of applications.
Consequently, a need still exists for a universal monitoring device utilizing a common, statistic-based algorithm that is not limited by particular type of machine, but uses general vibration properties common to all kinds of rotating machines.
The purpose of this invention is to develop a method of monitoring a rotating machine using the vibration signal and a variety of the digital signal processing methods.
To facilitate detection of the mechanical defects the motors mechanical vibration is converted to the electrical analog signal by a piezoceramic sensor, and then, after preconditioning, is converted to digital form.
The preconditioning of the signal consist of anti-alias filtering and preamplifying. The anti-alias filter has a cutoff frequency of 5 kHz, the high limit of the sensor frequency band is 10 kHz. The sampling rate of the applied 14-bit AD converter is 20 kHz. The collected data is recorded into a memory to be used for calculations.
The present invention utilizes collected vibration data to calculate a set of statistical parameters of vibration such as root mean square (RMS), kurtosis (KU), crest factor (CF), high frequency enveloping (HFE), as well as trending of the mean values of the selected areas of averaged spectra. The combination of the values is used for the calculation of two general output values characterizing a mechanical condition of the rotating machine. The first said value corresponds to the overall condition of the machine and the second value corresponds to the condition of the bearings. The second value may be used also to detect other mechanical problems characterized by the metal-to-metal impacts. Thus, the output of the method contains not only the set of data for the following analysis, but also a direct indication as to what has happened to the machine being monitored.